patatas 0.1.1

A powerful package for K-NN regression, data preprocessing, and analysis for Data Science

Patatas

https://pypi.org/project/patatas/

Patatas is a Python package that provides tools for preprocessing and modeling data using machine learning algorithms.

Installation

You can install Patatas using pip:

pip install patatas

Installation

Usage

Encoding categorical columns To encode all categorical (object) columns of a pandas DataFrame using Label Encoding, you can use the fritas() function:

from patatas import fritas
import pandas as pd

# Create a sample DataFrame with categorical columns
df = pd.DataFrame({'Color': ['Red', 'Green', 'Blue'], 'Size': ['Small', 'Medium', 'Large']})

# Encode categorical columns using Label Encoding
df_encoded = fritas(df)

# Show the encoded DataFrame
print(df_encoded)

Finding the best value of k for K-NN regression To find the best value of k (number of neighbors) for K-NN regression based on the mean squared error, you can use the bravas() function:

from patatas import bravas
import pandas as pd

# Load a sample dataset
df = pd.read_csv('my_dataset.csv')

# Find the best value of k for K-NN regression
best_k = bravas(df, 'target_column')
print(f'The best value of k is {best_k}')
Contributing
Contributions to Patata Poderosa are welcome! To contribute, please follow these steps:

Fork the repository and create a new branch for your feature or bug fix. Write tests for your changes. Implement your feature or bug fix. Run the tests and ensure they pass. Submit a pull request. License Patatas is released under the MIT License. See the LICENSE file for more details.